Completely different inputs to a neural network I'm looking to train a recurrent neural network model with several different types of input vector. The data includes the input vector of an image (307200x1), an input vector of the position/orientation of one object (7x1), and another input vector of the position/orientation of another object. These vectors have nothing to do with each other, so encoding is not really applicable here. I am trying to figure out if I just concatenate these vectors and feed that as an input for the RCNN, or if I should combine 3 RCNNs, one for each vector, somehow. Each of these three vectors are capable of accurately predicting the correct output of the model, but I would like to combine this information to get even more accurate. Thank you for your time.
 A: I assume by RCNN you're talking about a recurrent convolutional neural network.
I don't have direct experience with this type on neural network so take my advice with a grain of salt...
The 'convolutional' part of a convolutional NN is to help process the visual data. In a regular NN, there's no reason the system would suspect each entry in the 307200 x 1 vector to be related to each other, let alone be adjacent (or nearby) pixels in a 640x480 grid. 
Since the locational data of the two objects has nothing to do with machine seeing and only a little bit to do with which pixels are highlighted, it makes no sense to run them through convolutional layers, so I suspect neither of your proposed solutions would be what you're after.
As to how to construct your neural network, I don't have the experience to say how it would best be done. I'm imagining a NN with a large block of convoluted nodes where the location/orientation data comes in unaltered as a second set of neurons after the convolutional layer but before the layers used for deep learning. Having said that, I have no idea how you'd do that using any of the neural network tools I've worked with.
I know this doesn't fully answer the question, but I hope it helps.    
